This vignette describes how to analyse a mass-spectrometry based hydrogen deuterium exchange experiment, in particular we focus on empirical Bayes functional models and visualisations. This vignette descibes a case-study for analysing secA protein
hdxstats 0.99.1
Ahdash et al. performed HDX-MS on the baceterial sec translocon, a multi-protein complex responsible for translocating diverse proteins across the plasma membrane. Here we analyse this experiment using our functional data analysis approaches
We will first load the packages required in the analysis of this approach. Please install these packages if you do not have them.
The data is stored as a .csv, we need to covert it to an object of class
QFeatures. This is performed in a number of steps which we detail below. The
data is contained within the package and so can be loaded by specificy
the correct path.
#secApath <- system.file("inst/extdata", "Project_2_SecA_Cluster_Data.csv", package = "hdxstats")
secApath <- "../inst/extdata/Project_2_SecA_Cluster_Data.csv"
We can now read in the .csv file and have a quick look at the .csv.
secA <- read.csv(secApath)
head(secA) # have a look
## Protein Start End Sequence Modification Fragment MaxUptake MHP State
## 1 RANDOM 4 8 LGGTQ NA NA 4 475.2511 SecA
## 2 RANDOM 4 8 LGGTQ NA NA 4 475.2511 SecA
## 3 RANDOM 4 8 LGGTQ NA NA 4 475.2511 SecA
## 4 RANDOM 4 8 LGGTQ NA NA 4 475.2511 SecA
## 5 RANDOM 4 8 LGGTQ NA NA 4 475.2511 SecA
## 6 RANDOM 4 8 LGGTQ NA NA 4 475.2511 SecA
## Exposure File z RT Inten Center
## 1 0.00 291018_SecA_REF_5 1 3.693435 710541 475.5310
## 2 0.00 291018_SecA_REF_4 1 4.077553 13038 476.8321
## 3 0.00 291018_SecA_REF_3 1 3.719365 819312 475.4421
## 4 0.00 291018_SecA_REF_2 1 3.712732 559986 475.4829
## 5 0.00 291018_SecA_REF_1 1 3.677982 527423 475.5147
## 6 0.25 011118_SecA_15sec_1 1 3.724814 506213 475.9036
length(unique(secA$Sequence)) # peptide sequences
## [1] 397
unique(secA$State) # States
## [1] "SecA" "SecAYEG" "SecAYEG_ADP" "SecAYEG_AMPPNP"
## [5] "SecA_ADP" "SecA_AMPPNP"
Let us have a quick visualisation of some the data so that we can see some of the features
filter(secA, Sequence == unique(secA$Sequence[1]), z == 1) %>%
ggplot(aes(x = Exposure, y = Center, color = factor(State))) +
theme_classic() + geom_point(size = 2) +
scale_color_manual(values = brewer.pal(n = 7, name = "Set2")) +
labs(color = "experiment", x = "Deuterium Exposure", y = "Peptide Mass")
# Parsing to an object of class QFeatures
Working from a .csv is likely to cause issues downstream. Indeed, we run
the risk of accidently changing the data or corrupting the file in some way.
Secondly, all .csvs will be formatted slightly different and so making extensible
tools for these files will be inefficient. Furthermore, working with a generic
class used in other mass-spectrometry fields can speed up analysis and adoption
of new methods. We will work the class QFeatures from the QFeatures class
as it is a powerful and scalable way to store quantitative mass-spectrometry data.
Firstly, the data is storted in long format rather than wide format. We first switch the data to wide format.
secA <- pivot_wider(data.frame(secA),
values_from = "Center",
names_from = c("Exposure", "File", "State"),
id_cols = c("Sequence", "z"))
head(secA)
## # A tibble: 6 × 107
## Sequence z 0_291…¹ 0_291…² 0_291…³ 0_291…⁴ 0_291…⁵ 0.25_…⁶ 0.25_…⁷ 0.25_…⁸
## <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 LGGTQ 1 476. 477. 475. 475. 476. 476. 476. 476.
## 2 MLIKL 1 618. 618. 618. 618. 618. 620. 620. 620.
## 3 LIKLLT 1 701. 701. 701. 701. 701. 705. 705. 704.
## 4 IKLLT 1 588. 588. 588. 588. 588. 591. 591. 591.
## 5 IINAME 1 691. 691. 691. 691. 691. 691. NA 691.
## 6 AMEPEM 1 708. 708. 708. 708. 708. 708. 708. 708.
## # … with 97 more variables: `1_011118_SecA_1min_1_SecA` <dbl>,
## # `1_011118_SecA_1min_2_SecA` <dbl>, `1_011118_SecA_1min_3_SecA` <dbl>,
## # `5_011118_SecA_5min_1_SecA` <dbl>, `5_011118_SecA_5min_2_SecA` <dbl>,
## # `5_011118_SecA_5min_3_SecA` <dbl>,
## # `30.000002_011118_SecA_30min_1_SecA` <dbl>,
## # `30.000002_011118_SecA_30min_2_SecA` <dbl>,
## # `30.000002_011118_SecA_30min_3_SecA` <dbl>, …
We notice that there are many columns with NAs. The follow code chunk removes
these columns.
secA <- secA[, colSums(is.na(secA)) != nrow(secA)]
We also note that the colnames are not very informative. We are going to format in a very specific way so that later functions can automatically infer the design from the column names. We provide in the format X(time)rep(replicate)cond(condition)
colnames(secA[-c(1,2)])
## [1] "0_291018_SecA_REF_5_SecA"
## [2] "0_291018_SecA_REF_4_SecA"
## [3] "0_291018_SecA_REF_3_SecA"
## [4] "0_291018_SecA_REF_2_SecA"
## [5] "0_291018_SecA_REF_1_SecA"
## [6] "0.25_011118_SecA_15sec_1_SecA"
## [7] "0.25_011118_SecA_15sec_2_SecA"
## [8] "0.25_011118_SecA_15sec_3_SecA"
## [9] "1_011118_SecA_1min_1_SecA"
## [10] "1_011118_SecA_1min_2_SecA"
## [11] "1_011118_SecA_1min_3_SecA"
## [12] "5_011118_SecA_5min_1_SecA"
## [13] "5_011118_SecA_5min_2_SecA"
## [14] "5_011118_SecA_5min_3_SecA"
## [15] "30.000002_011118_SecA_30min_1_SecA"
## [16] "30.000002_011118_SecA_30min_2_SecA"
## [17] "30.000002_011118_SecA_30min_3_SecA"
## [18] "0_011118_SecA_YEG_ref_2_SecAYEG"
## [19] "0_011118_SecA_YEG_ref_3_SecAYEG"
## [20] "0_011118_SecA_YEG_ref_1_SecAYEG"
## [21] "0.25_011118_SecA_YEG_15sec_1_SecAYEG"
## [22] "0.25_011118_SecA_YEG_15sec_2_SecAYEG"
## [23] "0.25_011118_SecA_YEG_15sec_3_SecAYEG"
## [24] "1_011118_SecA_YEG_1min_1_SecAYEG"
## [25] "1_011118_SecA_YEG_1min_2_SecAYEG"
## [26] "1_011118_SecA_YEG_1min_3_SecAYEG"
## [27] "5_011118_SecA_YEG_5min_1_SecAYEG"
## [28] "5_011118_SecA_YEG_5min_2_SecAYEG"
## [29] "5_011118_SecA_YEG_5min_3_SecAYEG"
## [30] "30.000002_011118_SecA_YEG_30min_1_SecAYEG"
## [31] "30.000002_011118_SecA_YEG_30min_2_SecAYEG"
## [32] "30.000002_011118_SecA_YEG_30min_3_SecAYEG"
## [33] "0_011118_SecA_YEG_ref_2_SecAYEG_ADP"
## [34] "0_011118_SecA_YEG_ref_3_SecAYEG_ADP"
## [35] "0_011118_SecA_YEG_ref_1_SecAYEG_ADP"
## [36] "0.25_311018_SecA_YEG_ADP_15sec_3_SecAYEG_ADP"
## [37] "0.25_311018_SecA_YEG_ADP_15sec_2_SecAYEG_ADP"
## [38] "0.25_311018_SecA_YEG_ADP_15sec_1_SecAYEG_ADP"
## [39] "1_311018_SecA_YEG_ADP_1min_3_SecAYEG_ADP"
## [40] "1_311018_SecA_YEG_ADP_1min_2_SecAYEG_ADP"
## [41] "1_311018_SecA_YEG_ADP_1min_1_SecAYEG_ADP"
## [42] "5_311018_SecA_YEG_ADP_5min_3_SecAYEG_ADP"
## [43] "5_311018_SecA_YEG_ADP_5min_2_SecAYEG_ADP"
## [44] "5_311018_SecA_YEG_ADP_5min_1_SecAYEG_ADP"
## [45] "30.000002_311018_SecA_YEG_ADP_30min_3_SecAYEG_ADP"
## [46] "30.000002_311018_SecA_YEG_ADP_30min_2_SecAYEG_ADP"
## [47] "30.000002_311018_SecA_YEG_ADP_30min_1_SecAYEG_ADP"
## [48] "0_011118_SecA_YEG_ref_2_SecAYEG_AMPPNP"
## [49] "0_011118_SecA_YEG_ref_3_SecAYEG_AMPPNP"
## [50] "0_011118_SecA_YEG_ref_1_SecAYEG_AMPPNP"
## [51] "0.25_291018_SecYEGA_AMPPNP_15sec_3_SecAYEG_AMPPNP"
## [52] "0.25_291018_SecYEGA_AMPPNP_15sec_2_SecAYEG_AMPPNP"
## [53] "0.25_291018_SecYEGA_AMPPNP_15sec_1_SecAYEG_AMPPNP"
## [54] "1_291018_SecYEGA_AMPPNP_1min_3_SecAYEG_AMPPNP"
## [55] "1_291018_SecYEGA_AMPPNP_1min_2_SecAYEG_AMPPNP"
## [56] "1_291018_SecYEGA_AMPPNP_1min_1_SecAYEG_AMPPNP"
## [57] "5_301018_SecYEGA_AMPPNP_5min_3_SecAYEG_AMPPNP"
## [58] "5_301018_SecYEGA_AMPPNP_5min_2_SecAYEG_AMPPNP"
## [59] "5_301018_SecYEGA_AMPPNP_5min_1_SecAYEG_AMPPNP"
## [60] "30.000002_301018_SecYEGA_AMPPNP_30min_3_SecAYEG_AMPPNP"
## [61] "30.000002_301018_SecYEGA_AMPPNP_30min_2_SecAYEG_AMPPNP"
## [62] "30.000002_301018_SecYEGA_AMPPNP_30min_1_SecAYEG_AMPPNP"
## [63] "0_291018_SecA_REF_5_SecA_ADP"
## [64] "0_291018_SecA_REF_4_SecA_ADP"
## [65] "0_291018_SecA_REF_3_SecA_ADP"
## [66] "0_291018_SecA_REF_2_SecA_ADP"
## [67] "0_291018_SecA_REF_1_SecA_ADP"
## [68] "0.25_311018_SecA_ADP_15sec_4_SecA_ADP"
## [69] "0.25_311018_SecA_ADP_15sec_3_SecA_ADP"
## [70] "0.25_301018_SecA_ADP_15sec_2_SecA_ADP"
## [71] "1_311018_SecA_ADP_1min_3_SecA_ADP"
## [72] "1_311018_SecA_ADP_1min_2_SecA_ADP"
## [73] "1_311018_SecA_ADP_1min_1_SecA_ADP"
## [74] "5_311018_SecA_ADP_5min_3_SecA_ADP"
## [75] "5_311018_SecA_ADP_5min_2_SecA_ADP"
## [76] "5_311018_SecA_ADP_5min_1_SecA_ADP"
## [77] "30.000002_311018_SecA_ADP_30min_3_SecA_ADP"
## [78] "30.000002_311018_SecA_ADP_30min_2_SecA_ADP"
## [79] "30.000002_311018_SecA_ADP_30min_1_SecA_ADP"
## [80] "0_291018_SecA_REF_5_SecA_AMPPNP"
## [81] "0_291018_SecA_REF_4_SecA_AMPPNP"
## [82] "0_291018_SecA_REF_3_SecA_AMPPNP"
## [83] "0_291018_SecA_REF_2_SecA_AMPPNP"
## [84] "0_291018_SecA_REF_1_SecA_AMPPNP"
## [85] "0.25_291018_SecA_AMPPNP_15sec_3_SecA_AMPPNP"
## [86] "0.25_291018_SecA_AMPPNP_15sec_2_SecA_AMPPNP"
## [87] "0.25_291018_SecA_AMPPNP_15sec_1_SecA_AMPPNP"
## [88] "1_291018_SecA_AMPPNP_1min_3_SecA_AMPPNP"
## [89] "1_291018_SecA_AMPPNP_1min_2_SecA_AMPPNP"
## [90] "1_291018_SecA_AMPPNP_1min_1_SecA_AMPPNP"
## [91] "5_291018_SecA_AMPPNP_5min_3_SecA_AMPPNP"
## [92] "5_291018_SecA_AMPPNP_5min_2_SecA_AMPPNP"
## [93] "5_291018_SecA_AMPPNP_5min_1_SecA_AMPPNP"
## [94] "30.000002_291018_SecA_AMPPNP_30min_3_SecA_AMPPNP"
## [95] "30.000002_291018_SecA_AMPPNP_30min_2_SecA_AMPPNP"
## [96] "30.000002_291018_SecA_AMPPNP_30min_1_SecA_AMPPNP"
## [97] "0_291018_SecA_SecYEG_REF_2_SecAYEG"
## [98] "0_291018_SecA_SecYEG_REF_2_SecAYEG_ADP"
## [99] "0_291018_SecA_SecYEG_REF_2_SecAYEG_AMPPNP"
## [100] "0_291018_SecA_YEG_REF_1_SecAYEG"
## [101] "0_291018_SecA_YEG_REF_1_SecAYEG_ADP"
## [102] "0_291018_SecA_YEG_REF_1_SecAYEG_AMPPNP"
## [103] "0_291018_SecA_SecYEG_REF_3_SecAYEG"
## [104] "0_291018_SecA_SecYEG_REF_3_SecAYEG_ADP"
## [105] "0_291018_SecA_SecYEG_REF_3_SecAYEG_AMPPNP"
#################################
# DONE
new.colnames <- gsub("0_", "0rep", paste0("X", colnames(secA)[-c(1,2)]))
#################################
# DONE
new.colnames <- gsub("X0.25_", "X15rep", new.colnames) # convert to seconds too
new.colnames <- gsub("X1_", "X60rep", new.colnames) # convert to seconds too
new.colnames <- gsub("X5_", "X300rep", new.colnames) # convert to seconds too
new.colnames <- gsub("X30.000002_", "X1800rep", new.colnames) # convert to seconds too
#################################
# TEST
# States <- c("SecA", "SecAYEG", "SecAYEG_ADP", "SecAYEG_AMPPNP", "SecA_ADP", "SecA_AMPPNP")
for (i in 1:length(new.colnames)){
x <- strsplit(new.colnames[[i]], split = "SecA")
if (length(x[[1]]) == 3){
y <- tail(strsplit(x[[1]][[2]], split="_")[[1]], n=1)
}
else if (length(x[[1]]) == 2){
y <- tail(strsplit(x[[1]][[1]], split="_")[[1]], n=1)
}
print(paste(length(x[[1]]), new.colnames[[i]], x, y))
}
## [1] "2 X0rep291018_SecA_REF_5_SecA c(\"X0rep291018_\", \"_REF_5_\") X0rep291018"
## [1] "2 X0rep291018_SecA_REF_4_SecA c(\"X0rep291018_\", \"_REF_4_\") X0rep291018"
## [1] "2 X0rep291018_SecA_REF_3_SecA c(\"X0rep291018_\", \"_REF_3_\") X0rep291018"
## [1] "2 X0rep291018_SecA_REF_2_SecA c(\"X0rep291018_\", \"_REF_2_\") X0rep291018"
## [1] "2 X0rep291018_SecA_REF_1_SecA c(\"X0rep291018_\", \"_REF_1_\") X0rep291018"
## [1] "2 X15rep011118_SecA_15sec_1_SecA c(\"X15rep011118_\", \"_15sec_1_\") X15rep011118"
## [1] "2 X15rep011118_SecA_15sec_2_SecA c(\"X15rep011118_\", \"_15sec_2_\") X15rep011118"
## [1] "2 X15rep011118_SecA_15sec_3_SecA c(\"X15rep011118_\", \"_15sec_3_\") X15rep011118"
## [1] "2 X60rep011118_SecA_1min_1_SecA c(\"X60rep011118_\", \"_1min_1_\") X60rep011118"
## [1] "2 X60rep011118_SecA_1min_2_SecA c(\"X60rep011118_\", \"_1min_2_\") X60rep011118"
## [1] "2 X60rep011118_SecA_1min_3_SecA c(\"X60rep011118_\", \"_1min_3_\") X60rep011118"
## [1] "2 X300rep011118_SecA_5min_1_SecA c(\"X300rep011118_\", \"_5min_1_\") X300rep011118"
## [1] "2 X300rep011118_SecA_5min_2_SecA c(\"X300rep011118_\", \"_5min_2_\") X300rep011118"
## [1] "2 X300rep011118_SecA_5min_3_SecA c(\"X300rep011118_\", \"_5min_3_\") X300rep011118"
## [1] "2 X1800rep011118_SecA_30min_1_SecA c(\"X1800rep011118_\", \"_30min_1_\") X1800rep011118"
## [1] "2 X1800rep011118_SecA_30min_2_SecA c(\"X1800rep011118_\", \"_30min_2_\") X1800rep011118"
## [1] "2 X1800rep011118_SecA_30min_3_SecA c(\"X1800rep011118_\", \"_30min_3_\") X1800rep011118"
## [1] "3 X0rep011118_SecA_YEG_ref_2_SecAYEG c(\"X0rep011118_\", \"_YEG_ref_2_\", \"YEG\") 2"
## [1] "3 X0rep011118_SecA_YEG_ref_3_SecAYEG c(\"X0rep011118_\", \"_YEG_ref_3_\", \"YEG\") 3"
## [1] "3 X0rep011118_SecA_YEG_ref_1_SecAYEG c(\"X0rep011118_\", \"_YEG_ref_1_\", \"YEG\") 1"
## [1] "3 X15rep011118_SecA_YEG_15sec_1_SecAYEG c(\"X15rep011118_\", \"_YEG_15sec_1_\", \"YEG\") 1"
## [1] "3 X15rep011118_SecA_YEG_15sec_2_SecAYEG c(\"X15rep011118_\", \"_YEG_15sec_2_\", \"YEG\") 2"
## [1] "3 X15rep011118_SecA_YEG_15sec_3_SecAYEG c(\"X15rep011118_\", \"_YEG_15sec_3_\", \"YEG\") 3"
## [1] "3 X60rep011118_SecA_YEG_1min_1_SecAYEG c(\"X60rep011118_\", \"_YEG_1min_1_\", \"YEG\") 1"
## [1] "3 X60rep011118_SecA_YEG_1min_2_SecAYEG c(\"X60rep011118_\", \"_YEG_1min_2_\", \"YEG\") 2"
## [1] "3 X60rep011118_SecA_YEG_1min_3_SecAYEG c(\"X60rep011118_\", \"_YEG_1min_3_\", \"YEG\") 3"
## [1] "3 X300rep011118_SecA_YEG_5min_1_SecAYEG c(\"X300rep011118_\", \"_YEG_5min_1_\", \"YEG\") 1"
## [1] "3 X300rep011118_SecA_YEG_5min_2_SecAYEG c(\"X300rep011118_\", \"_YEG_5min_2_\", \"YEG\") 2"
## [1] "3 X300rep011118_SecA_YEG_5min_3_SecAYEG c(\"X300rep011118_\", \"_YEG_5min_3_\", \"YEG\") 3"
## [1] "3 X1800rep011118_SecA_YEG_30min_1_SecAYEG c(\"X1800rep011118_\", \"_YEG_30min_1_\", \"YEG\") 1"
## [1] "3 X1800rep011118_SecA_YEG_30min_2_SecAYEG c(\"X1800rep011118_\", \"_YEG_30min_2_\", \"YEG\") 2"
## [1] "3 X1800rep011118_SecA_YEG_30min_3_SecAYEG c(\"X1800rep011118_\", \"_YEG_30min_3_\", \"YEG\") 3"
## [1] "3 X0rep011118_SecA_YEG_ref_2_SecAYEG_ADP c(\"X0rep011118_\", \"_YEG_ref_2_\", \"YEG_ADP\") 2"
## [1] "3 X0rep011118_SecA_YEG_ref_3_SecAYEG_ADP c(\"X0rep011118_\", \"_YEG_ref_3_\", \"YEG_ADP\") 3"
## [1] "3 X0rep011118_SecA_YEG_ref_1_SecAYEG_ADP c(\"X0rep011118_\", \"_YEG_ref_1_\", \"YEG_ADP\") 1"
## [1] "3 X15rep311018_SecA_YEG_ADP_15sec_3_SecAYEG_ADP c(\"X15rep311018_\", \"_YEG_ADP_15sec_3_\", \"YEG_ADP\") 3"
## [1] "3 X15rep311018_SecA_YEG_ADP_15sec_2_SecAYEG_ADP c(\"X15rep311018_\", \"_YEG_ADP_15sec_2_\", \"YEG_ADP\") 2"
## [1] "3 X15rep311018_SecA_YEG_ADP_15sec_1_SecAYEG_ADP c(\"X15rep311018_\", \"_YEG_ADP_15sec_1_\", \"YEG_ADP\") 1"
## [1] "3 X60rep311018_SecA_YEG_ADP_1min_3_SecAYEG_ADP c(\"X60rep311018_\", \"_YEG_ADP_1min_3_\", \"YEG_ADP\") 3"
## [1] "3 X60rep311018_SecA_YEG_ADP_1min_2_SecAYEG_ADP c(\"X60rep311018_\", \"_YEG_ADP_1min_2_\", \"YEG_ADP\") 2"
## [1] "3 X60rep311018_SecA_YEG_ADP_1min_1_SecAYEG_ADP c(\"X60rep311018_\", \"_YEG_ADP_1min_1_\", \"YEG_ADP\") 1"
## [1] "3 X300rep311018_SecA_YEG_ADP_5min_3_SecAYEG_ADP c(\"X300rep311018_\", \"_YEG_ADP_5min_3_\", \"YEG_ADP\") 3"
## [1] "3 X300rep311018_SecA_YEG_ADP_5min_2_SecAYEG_ADP c(\"X300rep311018_\", \"_YEG_ADP_5min_2_\", \"YEG_ADP\") 2"
## [1] "3 X300rep311018_SecA_YEG_ADP_5min_1_SecAYEG_ADP c(\"X300rep311018_\", \"_YEG_ADP_5min_1_\", \"YEG_ADP\") 1"
## [1] "3 X1800rep311018_SecA_YEG_ADP_30min_3_SecAYEG_ADP c(\"X1800rep311018_\", \"_YEG_ADP_30min_3_\", \"YEG_ADP\") 3"
## [1] "3 X1800rep311018_SecA_YEG_ADP_30min_2_SecAYEG_ADP c(\"X1800rep311018_\", \"_YEG_ADP_30min_2_\", \"YEG_ADP\") 2"
## [1] "3 X1800rep311018_SecA_YEG_ADP_30min_1_SecAYEG_ADP c(\"X1800rep311018_\", \"_YEG_ADP_30min_1_\", \"YEG_ADP\") 1"
## [1] "3 X0rep011118_SecA_YEG_ref_2_SecAYEG_AMPPNP c(\"X0rep011118_\", \"_YEG_ref_2_\", \"YEG_AMPPNP\") 2"
## [1] "3 X0rep011118_SecA_YEG_ref_3_SecAYEG_AMPPNP c(\"X0rep011118_\", \"_YEG_ref_3_\", \"YEG_AMPPNP\") 3"
## [1] "3 X0rep011118_SecA_YEG_ref_1_SecAYEG_AMPPNP c(\"X0rep011118_\", \"_YEG_ref_1_\", \"YEG_AMPPNP\") 1"
## [1] "2 X15rep291018_SecYEGA_AMPPNP_15sec_3_SecAYEG_AMPPNP c(\"X15rep291018_SecYEGA_AMPPNP_15sec_3_\", \"YEG_AMPPNP\") 3"
## [1] "2 X15rep291018_SecYEGA_AMPPNP_15sec_2_SecAYEG_AMPPNP c(\"X15rep291018_SecYEGA_AMPPNP_15sec_2_\", \"YEG_AMPPNP\") 2"
## [1] "2 X15rep291018_SecYEGA_AMPPNP_15sec_1_SecAYEG_AMPPNP c(\"X15rep291018_SecYEGA_AMPPNP_15sec_1_\", \"YEG_AMPPNP\") 1"
## [1] "2 X60rep291018_SecYEGA_AMPPNP_1min_3_SecAYEG_AMPPNP c(\"X60rep291018_SecYEGA_AMPPNP_1min_3_\", \"YEG_AMPPNP\") 3"
## [1] "2 X60rep291018_SecYEGA_AMPPNP_1min_2_SecAYEG_AMPPNP c(\"X60rep291018_SecYEGA_AMPPNP_1min_2_\", \"YEG_AMPPNP\") 2"
## [1] "2 X60rep291018_SecYEGA_AMPPNP_1min_1_SecAYEG_AMPPNP c(\"X60rep291018_SecYEGA_AMPPNP_1min_1_\", \"YEG_AMPPNP\") 1"
## [1] "2 X300rep301018_SecYEGA_AMPPNP_5min_3_SecAYEG_AMPPNP c(\"X300rep301018_SecYEGA_AMPPNP_5min_3_\", \"YEG_AMPPNP\") 3"
## [1] "2 X300rep301018_SecYEGA_AMPPNP_5min_2_SecAYEG_AMPPNP c(\"X300rep301018_SecYEGA_AMPPNP_5min_2_\", \"YEG_AMPPNP\") 2"
## [1] "2 X300rep301018_SecYEGA_AMPPNP_5min_1_SecAYEG_AMPPNP c(\"X300rep301018_SecYEGA_AMPPNP_5min_1_\", \"YEG_AMPPNP\") 1"
## [1] "2 X1800rep301018_SecYEGA_AMPPNP_30min_3_SecAYEG_AMPPNP c(\"X1800rep301018_SecYEGA_AMPPNP_30min_3_\", \"YEG_AMPPNP\") 3"
## [1] "2 X1800rep301018_SecYEGA_AMPPNP_30min_2_SecAYEG_AMPPNP c(\"X1800rep301018_SecYEGA_AMPPNP_30min_2_\", \"YEG_AMPPNP\") 2"
## [1] "2 X1800rep301018_SecYEGA_AMPPNP_30min_1_SecAYEG_AMPPNP c(\"X1800rep301018_SecYEGA_AMPPNP_30min_1_\", \"YEG_AMPPNP\") 1"
## [1] "3 X0rep291018_SecA_REF_5_SecA_ADP c(\"X0rep291018_\", \"_REF_5_\", \"_ADP\") 5"
## [1] "3 X0rep291018_SecA_REF_4_SecA_ADP c(\"X0rep291018_\", \"_REF_4_\", \"_ADP\") 4"
## [1] "3 X0rep291018_SecA_REF_3_SecA_ADP c(\"X0rep291018_\", \"_REF_3_\", \"_ADP\") 3"
## [1] "3 X0rep291018_SecA_REF_2_SecA_ADP c(\"X0rep291018_\", \"_REF_2_\", \"_ADP\") 2"
## [1] "3 X0rep291018_SecA_REF_1_SecA_ADP c(\"X0rep291018_\", \"_REF_1_\", \"_ADP\") 1"
## [1] "3 X15rep311018_SecA_ADP_15sec_4_SecA_ADP c(\"X15rep311018_\", \"_ADP_15sec_4_\", \"_ADP\") 4"
## [1] "3 X15rep311018_SecA_ADP_15sec_3_SecA_ADP c(\"X15rep311018_\", \"_ADP_15sec_3_\", \"_ADP\") 3"
## [1] "3 X15rep301018_SecA_ADP_15sec_2_SecA_ADP c(\"X15rep301018_\", \"_ADP_15sec_2_\", \"_ADP\") 2"
## [1] "3 X60rep311018_SecA_ADP_1min_3_SecA_ADP c(\"X60rep311018_\", \"_ADP_1min_3_\", \"_ADP\") 3"
## [1] "3 X60rep311018_SecA_ADP_1min_2_SecA_ADP c(\"X60rep311018_\", \"_ADP_1min_2_\", \"_ADP\") 2"
## [1] "3 X60rep311018_SecA_ADP_1min_1_SecA_ADP c(\"X60rep311018_\", \"_ADP_1min_1_\", \"_ADP\") 1"
## [1] "3 X300rep311018_SecA_ADP_5min_3_SecA_ADP c(\"X300rep311018_\", \"_ADP_5min_3_\", \"_ADP\") 3"
## [1] "3 X300rep311018_SecA_ADP_5min_2_SecA_ADP c(\"X300rep311018_\", \"_ADP_5min_2_\", \"_ADP\") 2"
## [1] "3 X300rep311018_SecA_ADP_5min_1_SecA_ADP c(\"X300rep311018_\", \"_ADP_5min_1_\", \"_ADP\") 1"
## [1] "3 X1800rep311018_SecA_ADP_30min_3_SecA_ADP c(\"X1800rep311018_\", \"_ADP_30min_3_\", \"_ADP\") 3"
## [1] "3 X1800rep311018_SecA_ADP_30min_2_SecA_ADP c(\"X1800rep311018_\", \"_ADP_30min_2_\", \"_ADP\") 2"
## [1] "3 X1800rep311018_SecA_ADP_30min_1_SecA_ADP c(\"X1800rep311018_\", \"_ADP_30min_1_\", \"_ADP\") 1"
## [1] "3 X0rep291018_SecA_REF_5_SecA_AMPPNP c(\"X0rep291018_\", \"_REF_5_\", \"_AMPPNP\") 5"
## [1] "3 X0rep291018_SecA_REF_4_SecA_AMPPNP c(\"X0rep291018_\", \"_REF_4_\", \"_AMPPNP\") 4"
## [1] "3 X0rep291018_SecA_REF_3_SecA_AMPPNP c(\"X0rep291018_\", \"_REF_3_\", \"_AMPPNP\") 3"
## [1] "3 X0rep291018_SecA_REF_2_SecA_AMPPNP c(\"X0rep291018_\", \"_REF_2_\", \"_AMPPNP\") 2"
## [1] "3 X0rep291018_SecA_REF_1_SecA_AMPPNP c(\"X0rep291018_\", \"_REF_1_\", \"_AMPPNP\") 1"
## [1] "3 X15rep291018_SecA_AMPPNP_15sec_3_SecA_AMPPNP c(\"X15rep291018_\", \"_AMPPNP_15sec_3_\", \"_AMPPNP\") 3"
## [1] "3 X15rep291018_SecA_AMPPNP_15sec_2_SecA_AMPPNP c(\"X15rep291018_\", \"_AMPPNP_15sec_2_\", \"_AMPPNP\") 2"
## [1] "3 X15rep291018_SecA_AMPPNP_15sec_1_SecA_AMPPNP c(\"X15rep291018_\", \"_AMPPNP_15sec_1_\", \"_AMPPNP\") 1"
## [1] "3 X60rep291018_SecA_AMPPNP_1min_3_SecA_AMPPNP c(\"X60rep291018_\", \"_AMPPNP_1min_3_\", \"_AMPPNP\") 3"
## [1] "3 X60rep291018_SecA_AMPPNP_1min_2_SecA_AMPPNP c(\"X60rep291018_\", \"_AMPPNP_1min_2_\", \"_AMPPNP\") 2"
## [1] "3 X60rep291018_SecA_AMPPNP_1min_1_SecA_AMPPNP c(\"X60rep291018_\", \"_AMPPNP_1min_1_\", \"_AMPPNP\") 1"
## [1] "3 X300rep291018_SecA_AMPPNP_5min_3_SecA_AMPPNP c(\"X300rep291018_\", \"_AMPPNP_5min_3_\", \"_AMPPNP\") 3"
## [1] "3 X300rep291018_SecA_AMPPNP_5min_2_SecA_AMPPNP c(\"X300rep291018_\", \"_AMPPNP_5min_2_\", \"_AMPPNP\") 2"
## [1] "3 X300rep291018_SecA_AMPPNP_5min_1_SecA_AMPPNP c(\"X300rep291018_\", \"_AMPPNP_5min_1_\", \"_AMPPNP\") 1"
## [1] "3 X1800rep291018_SecA_AMPPNP_30min_3_SecA_AMPPNP c(\"X1800rep291018_\", \"_AMPPNP_30min_3_\", \"_AMPPNP\") 3"
## [1] "3 X1800rep291018_SecA_AMPPNP_30min_2_SecA_AMPPNP c(\"X1800rep291018_\", \"_AMPPNP_30min_2_\", \"_AMPPNP\") 2"
## [1] "3 X1800rep291018_SecA_AMPPNP_30min_1_SecA_AMPPNP c(\"X1800rep291018_\", \"_AMPPNP_30min_1_\", \"_AMPPNP\") 1"
## [1] "3 X0rep291018_SecA_SecYEG_REF_2_SecAYEG c(\"X0rep291018_\", \"_SecYEG_REF_2_\", \"YEG\") 2"
## [1] "3 X0rep291018_SecA_SecYEG_REF_2_SecAYEG_ADP c(\"X0rep291018_\", \"_SecYEG_REF_2_\", \"YEG_ADP\") 2"
## [1] "3 X0rep291018_SecA_SecYEG_REF_2_SecAYEG_AMPPNP c(\"X0rep291018_\", \"_SecYEG_REF_2_\", \"YEG_AMPPNP\") 2"
## [1] "3 X0rep291018_SecA_YEG_REF_1_SecAYEG c(\"X0rep291018_\", \"_YEG_REF_1_\", \"YEG\") 1"
## [1] "3 X0rep291018_SecA_YEG_REF_1_SecAYEG_ADP c(\"X0rep291018_\", \"_YEG_REF_1_\", \"YEG_ADP\") 1"
## [1] "3 X0rep291018_SecA_YEG_REF_1_SecAYEG_AMPPNP c(\"X0rep291018_\", \"_YEG_REF_1_\", \"YEG_AMPPNP\") 1"
## [1] "3 X0rep291018_SecA_SecYEG_REF_3_SecAYEG c(\"X0rep291018_\", \"_SecYEG_REF_3_\", \"YEG\") 3"
## [1] "3 X0rep291018_SecA_SecYEG_REF_3_SecAYEG_ADP c(\"X0rep291018_\", \"_SecYEG_REF_3_\", \"YEG_ADP\") 3"
## [1] "3 X0rep291018_SecA_SecYEG_REF_3_SecAYEG_AMPPNP c(\"X0rep291018_\", \"_SecYEG_REF_3_\", \"YEG_AMPPNP\") 3"
#################################
new.colnames <- gsub(new.colnames, pattern = "rep.*?SecA_", replacement = "rep")
new.colnames <- gsub(new.colnames, pattern = "rep.*?_[0-9]_", replacement = "repcond")
#################################
# NOTE: The sequence of numbers doesn't really matter for the analysis, although data exploration graphical outputs are expected to change
repnumber <- c(seq.int(5), rep(c(1,2,3), times = 19),
seq.int(5), rep(c(1,2,3), times = 4),
seq.int(5), rep(c(1,2,3), times = 4), rep(c(4,5,6), each = 3))
new.colnames <- stringr::str_replace_all(string = new.colnames,
pattern = "rep",
replacement = paste0("rep", repnumber))
#################################
new.colnames
## [1] "X0rep1condSecA" "X0rep2condSecA"
## [3] "X0rep3condSecA" "X0rep4condSecA"
## [5] "X0rep5condSecA" "X15rep1condSecA"
## [7] "X15rep2condSecA" "X15rep3condSecA"
## [9] "X60rep1condSecA" "X60rep2condSecA"
## [11] "X60rep3condSecA" "X300rep1condSecA"
## [13] "X300rep2condSecA" "X300rep3condSecA"
## [15] "X1800rep1condSecA" "X1800rep2condSecA"
## [17] "X1800rep3condSecA" "X0rep1condSecAYEG"
## [19] "X0rep2condSecAYEG" "X0rep3condSecAYEG"
## [21] "X15rep1condSecAYEG" "X15rep2condSecAYEG"
## [23] "X15rep3condSecAYEG" "X60rep1condSecAYEG"
## [25] "X60rep2condSecAYEG" "X60rep3condSecAYEG"
## [27] "X300rep1condSecAYEG" "X300rep2condSecAYEG"
## [29] "X300rep3condSecAYEG" "X1800rep1condSecAYEG"
## [31] "X1800rep2condSecAYEG" "X1800rep3condSecAYEG"
## [33] "X0rep1condSecAYEG_ADP" "X0rep2condSecAYEG_ADP"
## [35] "X0rep3condSecAYEG_ADP" "X15rep1condSecAYEG_ADP"
## [37] "X15rep2condSecAYEG_ADP" "X15rep3condSecAYEG_ADP"
## [39] "X60rep1condSecAYEG_ADP" "X60rep2condSecAYEG_ADP"
## [41] "X60rep3condSecAYEG_ADP" "X300rep1condSecAYEG_ADP"
## [43] "X300rep2condSecAYEG_ADP" "X300rep3condSecAYEG_ADP"
## [45] "X1800rep1condSecAYEG_ADP" "X1800rep2condSecAYEG_ADP"
## [47] "X1800rep3condSecAYEG_ADP" "X0rep1condSecAYEG_AMPPNP"
## [49] "X0rep2condSecAYEG_AMPPNP" "X0rep3condSecAYEG_AMPPNP"
## [51] "X15rep1condSecAYEG_AMPPNP" "X15rep2condSecAYEG_AMPPNP"
## [53] "X15rep3condSecAYEG_AMPPNP" "X60rep1condSecAYEG_AMPPNP"
## [55] "X60rep2condSecAYEG_AMPPNP" "X60rep3condSecAYEG_AMPPNP"
## [57] "X300rep1condSecAYEG_AMPPNP" "X300rep2condSecAYEG_AMPPNP"
## [59] "X300rep3condSecAYEG_AMPPNP" "X1800rep1condSecAYEG_AMPPNP"
## [61] "X1800rep2condSecAYEG_AMPPNP" "X1800rep3condSecAYEG_AMPPNP"
## [63] "X0rep1condSecA_ADP" "X0rep2condSecA_ADP"
## [65] "X0rep3condSecA_ADP" "X0rep4condSecA_ADP"
## [67] "X0rep5condSecA_ADP" "X15rep1condSecA_ADP"
## [69] "X15rep2condSecA_ADP" "X15rep3condSecA_ADP"
## [71] "X60rep1condSecA_ADP" "X60rep2condSecA_ADP"
## [73] "X60rep3condSecA_ADP" "X300rep1condSecA_ADP"
## [75] "X300rep2condSecA_ADP" "X300rep3condSecA_ADP"
## [77] "X1800rep1condSecA_ADP" "X1800rep2condSecA_ADP"
## [79] "X1800rep3condSecA_ADP" "X0rep1condSecA_AMPPNP"
## [81] "X0rep2condSecA_AMPPNP" "X0rep3condSecA_AMPPNP"
## [83] "X0rep4condSecA_AMPPNP" "X0rep5condSecA_AMPPNP"
## [85] "X15rep1condSecA_AMPPNP" "X15rep2condSecA_AMPPNP"
## [87] "X15rep3condSecA_AMPPNP" "X60rep1condSecA_AMPPNP"
## [89] "X60rep2condSecA_AMPPNP" "X60rep3condSecA_AMPPNP"
## [91] "X300rep1condSecA_AMPPNP" "X300rep2condSecA_AMPPNP"
## [93] "X300rep3condSecA_AMPPNP" "X1800rep1condSecA_AMPPNP"
## [95] "X1800rep2condSecA_AMPPNP" "X1800rep3condSecA_AMPPNP"
## [97] "X0rep4condSecAYEG" "X0rep4condSecAYEG_ADP"
## [99] "X0rep4condSecAYEG_AMPPNP" "X0rep5condSecAYEG"
## [101] "X0rep5condSecAYEG_ADP" "X0rep5condSecAYEG_AMPPNP"
## [103] "X0rep6condSecAYEG" "X0rep6condSecAYEG_ADP"
## [105] "X0rep6condSecAYEG_AMPPNP"
We will now parse the data into an object of class QFeatures, we have provided
a function to assist with this in the package. If you want to do this yourself
use the readQFeatures function from the QFeatures package.
secAqDF <- parseDeutData(object = DataFrame(secA),
design = new.colnames,
quantcol = 3:105, sequence = "Sequence", charge = "z")
We normalise the data to uptake values based on the “undeuterated” mass of the peptide
secA_newdf <- DataFrame(data.frame(assay(secAqDF) - apply(assay(secAqDF), 1, function(x) min(x, na.rm = TRUE))))
secA_newdf <- cbind(DataFrame(secA)[,1:2], secA_newdf)
secAqDF_norm <- parseDeutData(secA_newdf, design = new.colnames, quantcol = 3:105, sequence = "Sequence", charge = "z")
Normalised by exchange amides
secAqDF_norm1 <- normalisehdx(secAqDF_norm,
sequence = unique(secA$Sequence),
method = "pc")
To help us get used to the QFeatures we show how to generate a heatmap
of these data from this object:
pheatmap(t(assay(secAqDF_norm1)),
cluster_rows = FALSE,
cluster_cols = FALSE,
color = brewer.pal(n = 9, name = "BuPu"),
main = "secA heatmap",
fontsize = 14,
legend_breaks = c(0, 1, 2, 3, 4, 5, 6, max(assay(secAqDF))),
legend_labels = c("0", "1", "2", "3", "4", "5", "6", "Incorporation"))
# Analysis
To simplify the analysis, we subset to examine the protein secA and seA bound to ADP.
secAqDF_norm1_sub <- secAqDF_norm1[,c(1:17, 63:79)]
The hdxstats package uses an empirical Bayes functional approach to analyse
the data. We explain this idea in steps so that we can get an idea of the approach.
First we fit the parametric model to the data. This will allow us to explore
the HdxStatModel class.
res <- differentialUptakeKinetics(object = secAqDF_norm1_sub, #provide a QFeature object
feature = rownames(secAqDF_norm1_sub)[[1]][5], # which peptide to do we fit
start = list(a = NULL, b = NULL, d = NULL, p = 1)) # what are the starting parameter guesses
## Warning in differentialUptakeKinetics(object = secAqDF_norm1_sub, feature =
## rownames(secAqDF_norm1_sub)[[1]][5], : NAs introduced by coercion
Here, we see the HdxStatModel class, and that a Functional Model was applied
to the data and a total of 2 models were fitted.
res
## Object of class "HdxStatModel"
## Method: Functional Model
## Fitted 2
The nullmodel and alternative slots of an instance of HdxStatModel provide
the underlying fitted models. The method and formula slots provide vital
information about what analysis was performed. The vis slot provides a ggplot
object so that we can visualise the functional fits.
res@vis
Since this is a ggplot object, we can customise in the usual grammatical ways.
res@vis + scale_color_manual(values = brewer.pal(n = 8, name = "Set2")[-c(1,2)])
## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
We have seen the basic aspects of our functional modelling approach. We now
wish to roll out our method across all peptides in the experiment. The
fitUptakeKinetics function allows us to apply our modelling approach across
all the peptide in the experiment. We need to provide a QFeatures object
and the features for which we are fitting the model. The design will be extracted
from the column names or you can provide a design yourself. The parameter
initilisation should also be provided. Sometimes the model can’t be fit on the
kinetics. This is either because there is not enough data or through lack of
convergence. An error will be reported in these cases but this should not
perturb the user. You may wish to try a few starting values if there
excessive models that fail fitting.
res <- fitUptakeKinetics(object = secAqDF_norm1_sub,
feature = rownames(secAqDF_norm1_sub)[[1]],
start = list(a = NULL, b = NULL, d = NULL, p = 1))
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Error in nlsModel(formula, mf, start, wts) :
## singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Error in nlsModel(formula, mf, start, wts) :
## singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Error in nlsModel(formula, mf, start, wts) :
## singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Error in nlsModel(formula, mf, start, wts) :
## singular gradient matrix at initial parameter estimates
## Error in nlsModel(formula, mf, start, wts) :
## singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"
## Error in nlsModel(formula, mf, start, wts) :
## singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Error in nlsModel(formula, mf, start, wts) :
## singular gradient matrix at initial parameter estimates
## Error in nlsModel(formula, mf, start, wts) :
## singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "too few data points to fit model"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Error in nlsModel(formula, mf, start, wts) :
## singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Error in nlsModel(formula, mf, start, wts) :
## singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Error in nlsModel(formula, mf, start, wts) :
## singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Error in nlsModel(formula, mf, start, wts) :
## singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Error in nlsModel(formula, mf, start, wts) :
## singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Error in nlsModel(formula, mf, start, wts) :
## singular gradient matrix at initial parameter estimates
## Error in nlsModel(formula, mf, start, wts) :
## singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Error in nlsModel(formula, mf, start, wts) :
## singular gradient matrix at initial parameter estimates
## Error in nlsModel(formula, mf, start, wts) :
## singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## [1] "too few data points to fit model"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## [1] "too few data points to fit model"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## [1] "too few data points to fit model"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Error in nlsModel(formula, mf, start, wts) :
## singular gradient matrix at initial parameter estimates
## Error in nlsModel(formula, mf, start, wts) :
## singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.
## Error in nlsModel(formula, mf, start, wts) :
## singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## [1] "too few data points to fit model"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Error in nlsModel(formula, mf, start, wts) :
## singular gradient matrix at initial parameter estimates
## Error in nlsModel(formula, mf, start, wts) :
## singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Error in nlsModel(formula, mf, start, wts) :
## singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## [1] "too few data points to fit model"
## [1] "too few data points to fit model"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Error in nlsModel(formula, mf, start, wts) :
## singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"
## Error in nlsModel(formula, mf, start, wts) :
## singular gradient matrix at initial parameter estimates
## Error in nlsModel(formula, mf, start, wts) :
## singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## [1] "too few data points to fit model"
## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.
## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.
## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.
## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.
## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Error in nlsModel(formula, mf, start, wts) :
## singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## [1] "too few data points to fit model"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.
## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.
## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.
## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.
## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Error in nlsModel(formula, mf, start, wts) :
## singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Error in nlsModel(formula, mf, start, wts) :
## singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.
## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.
## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.
## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.
## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## [1] "too few data points to fit model"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## [1] "too few data points to fit model"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.
## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.
## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.
## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.
## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Error in nlsModel(formula, mf, start, wts) :
## singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"
## Warning in nls.lm(par = start, fn = FCT, jac = jac, control = control, lower = lower, : lmdif: info = 0. Improper input parameters.
## Error in nlsModel(formula, mf, start, wts) :
## singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Error in nlsModel(formula, mf, start, wts) :
## singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"
## [1] "too few data points to fit model"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Error in nlsModel(formula, mf, start, wts) :
## singular gradient matrix at initial parameter estimates
## [1] "Could not fit model, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## [1] "too few data points to fit model"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## [1] "too few data points to fit model"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
## Warning in max(data$value): no non-missing arguments to max; returning -Inf
## Warning in min(data$value): no non-missing arguments to min; returning Inf
## Warning in differentialUptakeKinetics(object = object, feature = x, start =
## start, : NAs introduced by coercion
## [1] "model fit failed, likely exessive missing values"
The code chunk above returns a class HdxStatModels indicating that a number
of models for peptide have been fit. This is simply a holder for a list
of HdxStatModel instances.
res
## Object of class "HdxStatModels"
## Number of models 208
We can easily examine individual fits by going to the underyling HdxStatModel
class:
res@statmodels[[4]]@vis
We now wish to apply statistical analysis to these fitted curves. Our approach
is an empirical Bayes testing procedure, which borrows information across peptides
to stablise variance estimates. Here, we need to provide the original data
that was analysed and the HdxStatModels class. The following code chunk
returns an object of class HdxStatRes. This object tell us that statistical
analysis was performed using our Functional model.
out <- processFunctional(object = secAqDF_norm1_sub, params = res)
## Warning in pf(q = Fstat, df1 = d1, df2 = d2, lower.tail = FALSE): NaNs produced
## Warning in pf(q = modFstat, df1 = d1, df2 = d2, lower.tail = FALSE): NaNs
## produced
## Warning in stats::optim(x = c(0.00499600199281143, 0.00127485964142953, : one-dimensional optimization by Nelder-Mead is unreliable:
## use "Brent" or optimize() directly
## Warning in densfun(x, parm, ...): NaNs produced
## Warning in densfun(x, parm, ...): NaNs produced
## Warning in densfun(x, parm, ...): NaNs produced
## Warning in stats::optim(x = c(0.0126744194629643, 0.000351634549827898, : one-dimensional optimization by Nelder-Mead is unreliable:
## use "Brent" or optimize() directly
## Warning in densfun(x, parm, ...): NaNs produced
## Warning in densfun(x, parm, ...): NaNs produced
## Warning in densfun(x, parm, ...): NaNs produced
out
## Object of class "HdxStatRes"
## Analysed using Functional model
The main slot of interest is the results slot which returns quantities of
interest such as p-values and fdr corrected p-values because of multiple testing.
The following is the DataFrame of interest.
out@results
## DataFrame with 208 rows and 9 columns
## Fstat.Fstat Fstat.numerator Fstat.denomenator pvals
## <list> <list> <list> <numeric>
## LGGTQ_1 0.39418 0.004996 0.0126744 8.10901e-01
## IINAME_1 3.62552 0.00127486 0.000351635 1.89956e-02
## AMEPEM_1 13.6476 0.00240748 0.000176403 4.02221e-06
## MEPEM_1 21.7177 0.0063718 0.000293391 5.65838e-08
## EKLSDEELKGKTAE_3 6.6861 8.53706e-05 1.27684e-05 9.17603e-04
## ... ... ... ... ...
## IFQSIG_1 0.114395 0.0051095 0.0446654 0.975564
## PGMQG_1 0.533322 0.0212795 0.0398999 0.713196
## VINPGFAF_1 0.75321 0.00637703 0.00846647 0.570389
## VTGTMFL_1 0.219675 0.216819 0.986999 0.922320
## VYAAQSTHLPLKVNM_3 0 0 -0.0287788 NaN
## fdr ebayes.pvals ebayes.fdr empirical.fdr
## <numeric> <numeric> <numeric> <numeric>
## LGGTQ_1 8.60803e-01 7.96702e-01 8.45730e-01 0.642286
## IINAME_1 3.00159e-02 1.57736e-02 2.53111e-02 0.613006
## AMEPEM_1 1.28092e-05 2.99583e-06 1.03356e-05 0.613006
## MEPEM_1 2.92821e-07 3.79322e-08 1.96299e-07 0.613006
## EKLSDEELKGKTAE_3 1.80899e-03 2.25208e-03 4.16232e-03 0.613006
## ... ... ... ... ...
## IFQSIG_1 1.000000 0.971751 1.000000 0.690643
## PGMQG_1 0.764930 0.682930 0.732469 0.632548
## VINPGFAF_1 0.618170 0.533620 0.578321 0.619953
## VTGTMFL_1 0.964244 0.907741 0.953820 0.662517
## VYAAQSTHLPLKVNM_3 NaN NaN NaN 1.000000
## fitcomplete
## <integer>
## LGGTQ_1 1
## IINAME_1 2
## AMEPEM_1 3
## MEPEM_1 4
## EKLSDEELKGKTAE_3 5
## ... ...
## IFQSIG_1 204
## PGMQG_1 205
## VINPGFAF_1 206
## VTGTMFL_1 207
## VYAAQSTHLPLKVNM_3 208
We can now examine the peptides for which the false discovery rate is less than 0.01
which(out@results$ebayes.fdr < 0.01)
## AMEPEM_1 MEPEM_1 EKLSDEELKGKTAE_3
## 3 4 5
## EKLSDEELKGKTAEF_2 RARLEKGEVL_3 RARLEKGEVLE_3
## 6 8 9
## RARLEKGEVLENL_2 MRHFDVQL_2 DVQLLGG_1
## 10 19 20
## LLGGMVL_1 LGGMVL_1 LGGMVLNE_1
## 21 22 23
## MVLNE_1 NERSIAEM_1 MRTGEGKTLTATL_2
## 24 25 26
## RTGEGKTLTATL_2 ATLPAYL_1 PAYL_1
## 27 28 29
## NALTGKGVHVVT_2 NALTGKGVHVVTVND_2 YLAQRDAENNRPLF_3
## 31 32 33
## YLAQRDAENNRPLFE_2 AQRDAENNRPLFE_2 FLGLTVG_1
## 34 35 38
## LGLTVG_1 LTVGINLPGMPAPAKREAY_3 TVGINLPGMPAPAKREAYAAD_3
## 39 40 41
## INLPGMPAPAKREAYAAD_2 LPGMPAP_1 ITYGTNNEYGFD_2
## 42 43 46
## NEYGF_1 NEYGFD_1 DYLRDNMAFSPEE_2
## 47 48 49
## NMAFSPEE_1 AFSPEE_1 VDEVD_1
## 52 53 55
## EVDSIL_1 IDEARTPLIISGPAE_3 IDEARTPLIISGPAEDSSE_2
## 56 58 59
## ARTPLIISGPAE_2 ARTPLIISGPAEDSSE_2 FQGEGHF_2
## 60 61 64
## GEGHF_1 LLVKEGIM_1 LLVKEGIMDE_2
## 65 70 71
## LVKEGIM_1 LVKEGIMDE_1 LVKEGIMDEGES_2
## 73 74 75
## LVKEGIMDEGESL_2 YSPANIM_1 MHHVTAAL_1
## 76 79 82
## AKEGVQIQNE_2 AKEGVQIQNENQTL_2 AKEGVQIQNENQTLAS_2
## 89 90 91
## IQNENQTL_1 SITFQNY_1 FQNY_1
## 92 94 97
## YEKLAGMTGTADTE_2 YEKLAGMTGTADTEA_2 FEFSS_1
## 100 101 102
## VVVPTNRPMIRKDLPDL_4 VVVPTNRPMIRKDLPDLVY_4 VPTNRPMIRKDLPDL_3
## 107 108 109
## VYMTE_1 VYMTEA_1 IIEDIKERTAKGQPVL_3
## 111 112 113
## QPVL_1 VGTISIE_1 SELVSN_1
## 114 115 119
## AAIVA_1 AAIVAQAGYPAA_1 IVAQAGYPAA_1
## 122 123 124
## IVAQAGYPAAVT_1 QAGYPAA_1 VTIATNM_1
## 125 126 131
## IVLGGS_1 IVLGGSW_1 ALENPTAEQ_1
## 133 134 137
## EAGGL_1 YLSM_1 YLSME_1
## 141 143 144
## IEHPWVTKAIANA_2 FDIRKQLL_2 FDIRKQLLE_2
## 147 148 149
## FDIRKQLLEY_2 YDDVANDQRRAIY_2 DDVANDQRRAIY_2
## 150 151 152
## SQRNELL_1 DVSETI_1 IREDVF_1
## 153 156 157
## KATIDAYIPPQSL_2 WDIPGL_1 WDIPGLQE_1
## 158 162 163
## WDIPGLQERL_2 PGLQ_1 QERLKNDFDLDL_2
## 164 165 166
## ERLKNDF_2 ERLKNDFDLDL_2 RLKNDF_2
## 167 168 169
## RLKNDFDLDL_2 DLDL_1 LDLPIAE_1
## 170 171 172
## PIAE_1 PIAEWL_1 WLDKEPEL_1
## 173 174 175
## HEETL_1 HEETLRE_2 RERILAQSIE_2
## 179 180 181
## RILAQSIE_1 LQTL_1 QTLDSL_1
## 182 184 185
## WKEHLAAM_1 WKEHLAAMD_2 AAMDY_1
## 187 188 189
## FAAML_1 LESLKY_1 ESLKYEVISTL_2
## 196 198 199
## EVISTL_1
## 200
Let us visualise some of these examples:
res@statmodels[[24]]@vis + res@statmodels[[25]]@vis
We an use a forest plot to examine the differneces
fp <- forestPlot(params = res@statmodels[[25]], condition = c(1, 2))
We can produce a table to actual numbers. We see that at all 4 timepoints the deuterium difference is negative, though the confidence intervals overlap with 0. Our functional approach is picking up this small but reproducible difference.
knitr::kable(fp$data)
| Estimate | confL | confU | rownames | condition | |
|---|---|---|---|---|---|
| a | 0.4960103 | 0.3518399 | 0.6401808 | a | 1 |
| b | 0.2640037 | 0.2070750 | 0.3209325 | b | 1 |
| d | 0.0059789 | -0.0087207 | 0.0206785 | d | 1 |
| p | 0.2679734 | 0.1539674 | 0.3819795 | p | 1 |
| a1 | 0.9256685 | -26.6011374 | 28.4524745 | a | 2 |
| b1 | 0.2444346 | -7.9140489 | 8.4029182 | b | 2 |
| d1 | 0.0057254 | -0.0069761 | 0.0184269 | d | 2 |
| p1 | 0.0616955 | -0.3292508 | 0.4526418 | p | 2 |
| 1 | 0.0000000 | -0.0064143 | 0.0064143 | Timepoint 0 | Deuterium Difference |
| 2 | -0.0153433 | -0.0395712 | 0.0088846 | Timepoint 15 | Deuterium Difference |
| 3 | 0.0181838 | -0.0056625 | 0.0420301 | Timepoint 60 | Deuterium Difference |
| 4 | 0.0777933 | 0.0658278 | 0.0897587 | Timepoint 300 | Deuterium Difference |
| 5 | 0.1293957 | 0.0762629 | 0.1825286 | Timepoint 1800 | Deuterium Difference |
We can make a Manhattan plot to better spatially visualize what’s happening.
#We need to provide an indication of "difference" so we can examine deprotected
# or prected regions
diffdata <- assay(secAqDF_norm1_sub)[,34] - assay(secAqDF_norm1_sub)[,17]
secA <- read.csv(secApath)
#carefully subset regions and sequences
region = secA[, c("Start", "End")]
region <- unique(region[paste0(secA$Sequence, "_", secA$z) %in% rownames(out@results),])
sequences = rownames(out@results)
sigplots <- manhattanplot(params = out,
sequences = sequences,
region = region,
difference = diffdata,
nrow = 1)
## Scale for 'x' is already present. Adding another scale for 'x', which will
## replace the existing scale.
sigplots[[1]] + plot_layout(guides = 'collect')
## Warning: Removed 1 rows containing missing values (geom_point).